Fault diagnosis and prognosis of steer-by-wire system based on finite state machine and extreme learning machine

Neural Computing and Applications - In this paper, an integrated condition monitoring method combining model-based fault diagnosis and data-driven prognosis is proposed for steer-by-wire (SBW)...     

基于机器学习的蜂窝网络故障管理框架及方法综述

网络故障管理旨在检测、识别和纠正网络中发生的错误状况,为用户获得可靠稳定的网络服务提供保障,近年来,如何利用机器学习方法进行蜂窝网络故障管理引起了广泛关注。首先介绍了蜂窝网络故障管理的研究背景,明确网络故障管理的流程和功能;接着介绍现有蜂窝网络故障管理框架;随后对现有机器学习在蜂窝网络故障管理中的方法研究进行评述,从故障管理周期入手,分别对实现故障检测、故障诊断以及故障预测的机器学习方法展开介绍、总结和对比分析,为相关领域的研究提供参考。     

Eye state recognition based on deep integrated neural network and transfer learning

Multimedia Tools and Applications - Eye state recognition is widely used in many fields, such as driver drowsiness recognition, facial expression classification, and human–computer interface...     

An image super-resolution deep learning network based on multi-level feature extraction module

Multimedia Tools and Applications - Due to the lack of depth of the super-resolution (SR) method based on shallow networks, the feature maps of different convolutional layers have similar receptive...     

基于有限状态自动机的Web服务组合

Web服务是Web上的特殊软件资源,可以被应用系统发现和调用.如何根据用户的需求(目标服务)来组合Web服务是研究中需要解决的重要问题.通过使用有限状态自动机,服务组成的社区中的状态和操作可以使用有限状态自动机来模拟,这样可以表示服务操作的内部和外部概要.结合确定性动态命题逻辑,可以根据已有的Web服务,解决目标服务的可组合问题,产生组合计划.同时讨论了算法的复杂性.     

采用长短期记忆网络的深度学习方法进行网页正文提取

针对复杂网页上主题信息被过多地与主题无关的广告、导航、版权等噪声信息隐藏的问题,提出一种基于长短期记忆的深度学习正文提取方法(LTE).首先,设计一种根据超文本标记语言(HTML)中标签信息的数据划分策略:通过遍历HTML代码的文档对象模型(DOM)树来根据DOM树结构划分每一个具有文本信息的文本块;然后,通过预训练模型对每一个内容块的从属关系进行表征;最后,这些标签会被输入到用这种格式的数据预先训练好的长短期记忆(LSTM)网络模型进行主要内容正文判别.实验结果证明,模型能够有效拟合已标记的数据集,在训练集中的F1分数能稳定在0.96以上;对于不存在于训练集中的网页格式,对其正文的预测准确度也比两个传统正文抽取工具Readability和Newspaper3k的分别高47.54、19.02个百分点.由实验结果可知,LTE能够有效提取出网页中的正文内容.     

采用长短期记忆网络的深度学习方法进行网页正文提取

针对复杂网页上主题信息被过多地与主题无关的广告、导航、版权等噪声信息隐藏的问题,提出一种基于长短期记忆的深度学习正文提取方法(LTE).首先,设计一种根据超文本标记语言(HTML)中标签信息的数据划分策略:通过遍历HTML代码的文档对象模型(DOM)树来根据DOM树结构划分每一个具有文本信息的文本块;然后,通过预训练模型对每一个内容块的从属关系进行表征;最后,这些标签会被输入到用这种格式的数据预先训练好的长短期记忆(LSTM)网络模型进行主要内容正文判别.实验结果证明,模型能够有效拟合已标记的数据集,在训练集中的F1分数能稳定在0.96以上;对于不存在于训练集中的网页格式,对其正文的预测准确度也比两个传统正文抽取工具Readability和Newspaper3k的分别高47.54、19.02个百分点.由实验结果可知,LTE能够有效提取出网页中的正文内容.     

Robust encoder-decoder learning framework for offline handwritten mathematical expression recognition based on a multi-scale deep neural network

Dear editor, Mathematical expressions have been widely employed in scientific research, finance, and statistics, and play a significant role in educational acti...     

Optimal 5G network slicing using machine learning and deep learning concepts

Network slicing is predetermined to hold up the diversity of emerging applications with enhanced performance and flexibility requirements in the way of splitting the physical network into numerous logical networks. Consequently, a tremendous data count has been generated with an enormous number of mobile phones due to these applications. This has made remarkable challenges and has a considerable influence on the network slicing performance. This work aims to design an efficient network slicing using a hybrid learning algorithm. Thus, we proposed a model, which involves three main phases: (a) Data collection, (b) Optimal weighted feature extraction (OWFE), and (c) Slicing classification. First, we collected the 5G network slicing dataset, which involves the attributes associated with various network devices like “user device type, duration, packet loss ratio, packet delay budget, bandwidth, delay rate, speed, jitter, and modulation type.” Next, we performed the OWFE, in which a weight function is multiplied with the attribute values to have high scale variation. We optimized this weight function by the hybridization of two meta-heuristic algorithms—glowworm swarm optimization and deer hunting optimization algorithm (DHOA)—and named the proposed model glowworm swarm-based DHOA (GS-DHOA). For the given attributes, we classified the exact network slices like “eMBB, mMTC, and URLLC” for each device by a hybrid classifier using deep belief and neural networks. The weight function of both networks is optimized by the GS-DHOA. The experiment results revealed that the proposed model could influence the provision of accurate 5G network slicing.     

基于有限状态机的用户权限隔离模型

针对操作系统中的权限问题,提出了基于有限状态机(FSM)的用户权限隔离模型,将用户的授权访问行为刻画为一个有限状态机,任意用户的有限状态机都只能识别该用户的合法操作序列;同时,模型证明在用户权限交集的部分,即用户访问发生共享的点,容易出现权限窃取或者非法提升等安全问题。最终,利用有限状态机实现了对用户权限隔离的有效识别与判定。     

基于扩展有限状态机模型的故障检测

提出了基于扩展有限状态机的故障检测模型和检测算法。该模型对软件中的故障进行了形式化定义和描述,检测算法对故障模型中的状态变迁进行缩减,检测故障模型的部分状态变迁,进而可以有效缓解状态空间过大而引起的时间和空间效率问题,从而最大限度地发现被测系统中的故障;最后给出了一个简单协议来加以分析和验证。实验表明,该算法可以快速准确地定位软件中故障发生的位置。     

Side-channel analysis attacks based on deep learning network

There has been a growing interest in the side-channel analysis (SCA) field based on deep learning (DL) technology. Various DL network or model has been developed to improve the efficiency of SCA. However, few studies have investigated the impact of the different models on attack results and the exact relationship between power consumption traces and intermediate values. Based on the convolutional neural network and the autoencoder, this paper proposes a Template Analysis Pre-trained DL Classification model named TAPDC which contains three sub-networks. The TAPDC model detects the periodicity of power trace, relating power to the intermediate values and mining the deeper features by the multi-layer convolutional net. We implement the TAPDC model and compare it with two classical models in a fair experiment. The evaluative results show that the TAPDC model with autoencoder and deep convolution feature extraction structure in SCA can more effectively extract information from power consumption trace. Also, Using the classifier layer, this model links power information to the probability of intermediate value. It completes the conversion from power trace to intermediate values and greatly improves the efficiency of the power attack.     

基于深度卷积神经网络的道路场景理解

在无人驾驶技术中,道路场景的理解是一个非常重要的环境感知任务,也是一个很具有挑战性的课题。提出了一个深层的道路场景分割网络（Road Scene Segmentation Network,RSSNet）,该网络为32层的全卷积神经网络,由卷积编码网络和反卷积解码网络组成。网络中采用批正则化层防止了深度网络在训练中容易出现的“梯度消失”问题;在激活层中采用了Maxout激活函数,进一步缓解了梯度消失,避免网络陷入饱和模式以及出现神经元死亡现象;同时在网络中适当使用Dropout操作,防止了模型出现过拟合现象;编码网络存储了特征图的最大池化索引并在解码网络中使用它们,保留了重要的边缘信息。实验证明,该网络能够大大提高训练效率和分割精度,有效识别道路场景图像中各像素的类别并对目标进行平滑分割,为无人驾驶汽车提供有价值的道路环境信息。     

基于深度学习的事件因果关系抽取综述

因果关系抽取是自然语言处理(NLP)中的一种关系抽取任务,它通过构造事件图来挖掘文本中具有因果关系的事件对,已经在金融、安全、生物等领域的应用中发挥重要作用.首先,介绍了事件抽取和因果关系等概念,并介绍了因果关系抽取主流方法的演变和常用数据集;然后,列举了当前主流的因果关系抽取模型,并且在分别对基于流水线的模型和联合抽...     

A framework for machine learning based on dynamic physical fields

Despite recent successes and advancements in artificial intelligence and machine learning, this domain remains under continuous challenge and guidance from phenomena and processes observed in natural world. Humans remain unsurpassed in their efficiency of dealing and learning from uncertain information coming in a variety of forms, whereas more and more robust learning and optimisation algorithms have their analytical engine built on the basis of some nature-inspired phenomena. Excellence of neural networks and kernel-based learning methods, an emergence of particle-, swarms-, and social behaviour-based optimisation methods are just few of many facts indicating a trend towards greater exploitation of nature inspired models and systems. This work intends to demonstrate how a simple concept of a physical field can be adopted to build a complete framework for supervised and unsupervised learning methodology. An inspiration for artificial learning has been found in the mechanics of physical fields found on both micro and macro scales. Exploiting the analogies between data and charged particles subjected to gravity, electrostatic and gas particle fields, a family of new algorithms has been developed and applied to classification, clustering and data condensation while properties of the field were further used in a unique visualisation of classification and classifier fusion models. The paper covers extensive pictorial examples and visual interpretations of the presented techniques along with some comparative testing over well-known real and artificial datasets.

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms

Neural Computing and Applications - A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart...     

基于有限状态机和Trie数的分级地址模型

针对地理编码系统中地址正确性校验、地址不规则命名和地址跳跃的问题,提出了运用有限状态机理论建立分级地址的转换模型,同时用Trie树来建立有限状态机中各个地址的转换函数,给出了转换函数的初始化和训练过程。测试数据对模型的验证表明,使用有限状态机和Trie树建立的地址模型,初步解决了地理系统编码中的地址校验、不规则命名和地址跳跃的问题。     

基于Verilog HDL的有限状态机设计与描述

有限状态机(FSM)是逻辑设计的重要内容,稍大一点的逻辑设计都存在FSM.介绍了采用Verilog HDL实现有限状态机的几种不同编码方式和描述风格,并从稳定性、可读性、速度和面积等方面比较了不同实现方式的利弊.最后,以简单序列检测器为例实现了可综合的FSM描述,并分析了其采用不同描述风格所得的综合结果.     

A stock market trading framework based on deep learning architectures

Multimedia Tools and Applications - Market prediction has been a key interest for professionals around the world. Numerous modern technologies have been applied in addition to statistical models...